数据竞赛房租预测——数据清洗

最新推荐文章于 2022-04-28 09:35:42 发布

翻译最新推荐文章于 2022-04-28 09:35:42 发布 · 286 阅读

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本文详细介绍了数据预处理过程，包括缺失值、异常值的处理方法，如填充、编码、分割及异常值检测。并通过实例展示了如何使用Python进行数据清洗，确保数据质量。

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缺失值分析及处理

缺失值出现的原因分析
采取合适的方式对缺失值进行填充

异常值分析及处理

根据测试集数据的分布处理训练集的数据分布
使用合适的方法找出异常值
对异常值进行处理

深度清洗

分析每一个communityName、city、region、plate的数据分布并对其进行数据清洗

任务二基于任务一的分析做数据清洗。两步操作统称EDA

主要思路分析

虽然这步骤是缺失值处理，但还会涉及到一些最最基础的数据处理。

缺失值处理
缺失值的处理手段大体可以分为：删除、填充、映射到高维(当做类别处理)。
详细的请自行查找相关资料学习。
根据任务一，直接找到的缺失值情况是pu和pv；但是，根据特征nunique分布的分析，可以发现rentType存在"--"的情况，这也算是一种缺失值。
此外，诸如rentType的"未知方式"；houseToward的"暂无数据"等，本质上也算是一种缺失值，但是对于这些缺失方式，我们可以把它当做是特殊的一类处理，而不需要去主动修改或填充值。

将rentType的"--"转换成"未知方式"类别；
pv/pu的缺失值用均值填充；
buildYear存在"暂无信息"，将其用众数填充。

转换object类型数据
这里直接采用LabelEncoder的方式编码，详细的编码方式请自行查阅相关资料学习。

时间字段的处理
buildYear由于存在"暂无信息",所以需要主动将其转换int类型；
tradeTime，将其分割成月和日。

删除无关字段
ID是唯一码，建模无用，所以直接删除；
city只有一个SH值，也直接删除；
tradeTime已经分割成月和日，删除原来字段

preprocessingData()函数

def preprocessingData(data):
    # 填充缺失值
    data['rentType'][data['rentType'] == '--'] = '未知方式'
    
    # 转换object类型数据
    columns = ['rentType','communityName','houseType', 'houseFloor', 'houseToward', 'houseDecoration',  'region', 'plate']
    
    for feature in columns:
        data[feature] = LabelEncoder().fit_transform(data[feature])

    # 将buildYear列转换为整型数据
    buildYearmean = pd.DataFrame(data[data['buildYear'] != '暂无信息']['buildYear'].mode())
    data.loc[data[data['buildYear'] == '暂无信息'].index, 'buildYear'] = buildYearmean.iloc[0, 0]
    data['buildYear'] = data['buildYear'].astype('int')

    # 处理pv和uv的空值
    data['pv'].fillna(data['pv'].mean(), inplace=True)
    data['uv'].fillna(data['uv'].mean(), inplace=True)
    data['pv'] = data['pv'].astype('int')
    data['uv'] = data['uv'].astype('int')

    # 分割交易时间
    def month(x):
        month = int(x.split('/')[1])
        return month
    def day(x):
        day = int(x.split('/')[2])
        return day
    data['month'] = data['tradeTime'].apply(lambda x: month(x))
    data['day'] = data['tradeTime'].apply(lambda x: day(x))
    
    # 去掉部分特征
    data.drop('city', axis=1, inplace=True)
    data.drop('tradeTime', axis=1, inplace=True)
    data.drop('ID', axis=1, inplace=True)
    return data

data_train = preprocessingData(data_train)

填充缺失值、转换数据类型、处理空值、分割数据、去除特征

异常值处理

IF_drop（）

# clean data
def IF_drop(train):
    IForest = IsolationForest(contamination=0.01)
    IForest.fit(train["tradeMoney"].values.reshape(-1,1))
    y_pred = IForest.predict(train["tradeMoney"].values.reshape(-1,1))
    drop_index = train.loc[y_pred==-1].index
    print(drop_index)
    train.drop(drop_index,inplace=True)
    return train

dropData()

def dropData(train):
    # 丢弃部分异常值
    train = train[train.area <= 200]
    train = train[(train.tradeMoney <=16000) & (train.tradeMoney >=700)]
    train.drop(train[(train['totalFloor'] == 0)].index, inplace=True)
    return train

深度清洗

def cleanData(data):
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']<1000)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>250)&(data['tradeMoney']<20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00002') & (data['area']<100)&(data['tradeMoney']>60000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']<300)&(data['tradeMoney']>30000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<1500)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<2000)&(data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']>5000)&(data['area']<20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']>600)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']<1000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<2000)&(data['area']>180)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>200)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['area']>100)&(data['tradeMoney']<2500)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>200)&(data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>400)&(data['tradeMoney']<15000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']<3000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>7000)&(data['area']<75)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>12500)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['area']>400)&(data['tradeMoney']>20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008') & (data['tradeMoney']<2000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00011') & (data['tradeMoney']<10000)&(data['area']>390)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['area']>120)&(data['tradeMoney']<5000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']<100)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>80)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['area']>300)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1300)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<8000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1000)&(data['area']>20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']>25000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<20000)&(data['area']>250)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>30000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<50000)&(data['area']>600)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']>350)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']>4000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<600)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>165)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['tradeMoney']<800)&(data['area']<30)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['tradeMoney']<1100)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']>8000)&(data['area']<80)].index,inplace=True)
    data.loc[(data['region']=='RG00002')&(data['area']>50)&(data['rentType']=='合租'),'rentType']='整租'
    data.loc[(data['region']=='RG00014')&(data['rentType']=='合租')&(data['area']>60),'rentType']='整租'
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>15000)&(data['area']<110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>20000)&(data['area']>110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']<1500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['rentType']=='合租')&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00015') ].index,inplace=True)
    data.reset_index(drop=True, inplace=True)
    return data

data_train = cleanData(data_train)

针对每一个region的数据，对area和tradeMoney两个维度进行深度清洗。采用主观+数据可视化的方式。